Detailed Survival analyis of the Survival lung data.

Libraries

library(survival)
library(FRESA.CAD)
## Loading required package: Rcpp
## Loading required package: stringr
## Loading required package: miscTools
## Loading required package: Hmisc
## 
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
## 
##     format.pval, units
## Loading required package: pROC
## Type 'citation("pROC")' for a citation.
## 
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('keep.trailing.zeros',TRUE)

Libraries

data(lung)
## Warning in data(lung): data set 'lung' not found
lung$inst <- NULL
lung$status <- lung$status - 1
lung <- lung[complete.cases(lung),]

pander::pander(table(lung$status))
0 1
47 121
pander::pander(summary(lung$time))
Min. 1st Qu. Median Mean 3rd Qu. Max.
5 175 268 310 416 1022

Exploring Raw Features with RRPlot

convar <- colnames(lung)[lapply(apply(lung,2,unique),length) > 10]
convar <- convar[convar != "time"]
topvar <- univariate_BinEnsemble(lung[,c("status",convar)],"status")
pander::pander(topvar)
age wt.loss
0.106 0.106
topv <- min(5,length(topvar))
topFive <- names(topvar)[1:topv]
RRanalysis <- list();
idx <- 1
for (topf in topFive)
{
  RRanalysis[[idx]] <- RRPlot(cbind(lung$status,lung[,topf]),
                              atRate=c(0.90),
                  timetoEvent=lung$time,
                  title=topf,
#                  plotRR=FALSE
                  )
  idx <- idx + 1
}

names(RRanalysis) <- topFive

Reporting the Metrics

ROCAUC <- NULL
CstatCI <- NULL
LogRangp <- NULL
Sensitivity <- NULL
Specificity <- NULL

for (topf in topFive)
{
  CstatCI <- rbind(CstatCI,RRanalysis[[topf]]$c.index$cstatCI)
  LogRangp <- rbind(LogRangp,RRanalysis[[topf]]$surdif$pvalue)
  Sensitivity <- rbind(Sensitivity,RRanalysis[[topf]]$ROCAnalysis$sensitivity)
  Specificity <- rbind(Specificity,RRanalysis[[topf]]$ROCAnalysis$specificity)
  ROCAUC <- rbind(ROCAUC,RRanalysis[[topf]]$ROCAnalysis$aucs)
}
rownames(CstatCI) <- topFive
rownames(LogRangp) <- topFive
rownames(Sensitivity) <- topFive
rownames(Specificity) <- topFive
rownames(ROCAUC) <- topFive

pander::pander(ROCAUC)
  est lower upper
age 0.59 0.492 0.687
wt.loss 0.56 0.461 0.658
pander::pander(CstatCI)
  mean.C Index median lower upper
age 0.558 0.557 0.498 0.617
wt.loss 0.514 0.514 0.450 0.569
pander::pander(LogRangp)
age 0.919
wt.loss 0.358
pander::pander(Sensitivity)
  est lower upper
age 0.1157 0.0647 0.187
wt.loss 0.0496 0.0184 0.105
pander::pander(Specificity)
  est lower upper
age 0.872 0.743 0.952
wt.loss 0.894 0.769 0.965
meanMatrix <- cbind(ROCAUC[,1],CstatCI[,1],Sensitivity[,1],Specificity[,1])
colnames(meanMatrix) <- c("ROCAUC","C-Stat","Sen","Spe")
pander::pander(meanMatrix)
  ROCAUC C-Stat Sen Spe
age 0.59 0.558 0.1157 0.872
wt.loss 0.56 0.514 0.0496 0.894

Modeling

ml <- BSWiMS.model(Surv(time,status)~1,data=lung,NumberofRepeats = 10)

[+++++++++++++++++++-++++++++]..

sm <- summary(ml)
pander::pander(sm$coefficients)
Table continues below
  Estimate lower HR upper u.Accuracy r.Accuracy
ph.ecog 4.32e-01 1.194 1.541 1.988 0.679 0.649
sex -4.59e-01 0.456 0.632 0.876 0.649 0.679
pat.karno -1.77e-03 0.997 0.998 1.000 0.506 0.720
ph.karno -2.90e-07 1.000 1.000 1.000 0.577 0.720
age 9.13e-08 1.000 1.000 1.000 0.565 0.720
Table continues below
  full.Accuracy u.AUC r.AUC full.AUC IDI NRI
ph.ecog 0.601 0.601 0.620 0.600 0.0449 0.405
sex 0.601 0.620 0.601 0.600 0.0285 0.478
pat.karno 0.506 0.585 0.500 0.585 0.0292 0.342
ph.karno 0.577 0.570 0.500 0.570 0.0143 0.280
age 0.565 0.549 0.500 0.549 0.0162 0.195
  z.IDI z.NRI Delta.AUC Frequency
ph.ecog 3.33 2.48 -0.02005 1.0
sex 2.76 2.85 -0.00167 1.0
pat.karno 2.44 2.24 0.08546 1.0
ph.karno 2.22 1.64 0.06998 0.5
age 1.97 1.14 0.04871 0.2

Cox Model Performance

Here we evaluate the model using the RRPlot() function.

The evaluation of the raw Cox model with RRPlot()

Here we will use the predicted event probability assuming a baseline hazard for events withing 5 years

timeinterval <- 2*mean(subset(lung,status==1)$time)

h0 <- sum(lung$status & lung$time <= timeinterval)
h0 <- h0/sum((lung$time > timeinterval) | (lung$status==1))
pander::pander(t(c(h0=h0,timeinterval=timeinterval)),caption="Initial Parameters")
Initial Parameters
h0 timeinterval
0.85 578
index <- predict(ml,lung)

rdata <- cbind(lung$status,ppoisGzero(index,h0))

rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Raw Train: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

toinclude <- rdata[,1]==1 
obstiemToEvent <- lung$time
tmin<-min(obstiemToEvent)
sum(toinclude)

[1] 121

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)
  Estimate Std. Error t value Pr(>|t|)
timetoEvent[toinclude] 0.808 0.0509 15.9 2.92e-31
Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
121 200 0.677 0.675
plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <- mean(abs(timetoEvent[toinclude]-obstiemToEvent[toinclude]))
pander::pander(MADerror2)

163

Uncalibrated Performance Report

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.649 0.478 3.39e-01 3.39e-01 0.493
RR 1.214 1.742 6.85e+01 6.85e+01 1.270
RR_LCI 1.014 1.260 1.44e-01 1.44e-01 1.037
RR_UCI 1.454 2.408 3.26e+04 3.26e+04 1.555
SEN 0.314 0.826 1.00e+00 1.00e+00 0.612
SPE 0.830 0.511 1.91e-01 1.91e-01 0.596
BACC 0.572 0.669 5.96e-01 5.96e-01 0.604
NetBenefit 0.138 0.470 6.04e-01 6.04e-01 0.331
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.65 1.37 1.97 3.16e-07
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.23 1.23 1.19 1.27
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1.2 1.2 1.19 1.21
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.652 0.589 0.709
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.648
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cox Calibration

op <- par(no.readonly = TRUE)


calprob <- CoxRiskCalibration(ml,lung,"status","time")

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
1.29 1.52 750

The RRplot() of the calibrated model

h0 <- calprob$h0
timeinterval <- calprob$timeInterval;

rdata <- cbind(lung$status,calprob$prob)


rrAnalysisTrain <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=lung$time,
                     title="Train Cal: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Expected time to event

timetoEvent <- meanTimeToEvent(rdata[,2],timeinterval)
tmax<-max(c(obstiemToEvent,timetoEvent))
lmfit <- lm(obstiemToEvent[toinclude]~0+timetoEvent[toinclude])
sm <- summary(lmfit)
pander::pander(sm)
  Estimate Std. Error t value Pr(>|t|)
timetoEvent[toinclude] 0.947 0.0597 15.9 2.92e-31
Fitting linear model: obstiemToEvent[toinclude] ~ 0 + timetoEvent[toinclude]
Observations Residual Std. Error \(R^2\) Adjusted \(R^2\)
121 200 0.677 0.675
plot(timetoEvent,obstiemToEvent,
     col=1+rdata[,1],
     xlab="Expected time",
     ylab="Observed time",
     main="Expected vs. Observed",
     xlim=c(tmin,tmax),
     ylim=c(tmin,tmax),
     log="xy")
lines(x=c(tmin,tmax),y=lmfit$coefficients*c(tmin,tmax),lty=1,col="blue")
txt <- bquote(paste(R^2 == .(round(sm$r.squared,3))))
text(tmin+0.005*(tmax-tmin),tmax,txt,cex=0.7)
text(tmin+0.015*(tmax-tmin),tmax,sprintf("Slope=%4.3f",sm$coefficients[1]),cex=0.7)
legend("bottomright",legend=c("No Event","Event","Linear fit"),
             pch=c(1,1,-1),
             col=c(1,2,"blue"),
             lty=c(-1,-1,1)
             )

MADerror2 <-c(MADerror2,mean(abs(timetoEvent-obstiemToEvent)))
pander::pander(MADerror2)

163 and 169

Calibrated Train Performance

pander::pander(t(rrAnalysisTrain$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.7963 0.628 4.67e-01 4.67e-01 0.479
RR 1.2142 1.742 6.85e+01 6.85e+01 2.784
RR_LCI 1.0143 1.260 1.44e-01 1.44e-01 1.315
RR_UCI 1.4536 2.408 3.26e+04 3.26e+04 5.893
SEN 0.3140 0.826 1.00e+00 1.00e+00 0.959
SPE 0.8298 0.511 1.91e-01 1.91e-01 0.277
BACC 0.5719 0.669 5.96e-01 5.96e-01 0.618
NetBenefit 0.0401 0.365 5.22e-01 5.22e-01 0.504
pander::pander(t(rrAnalysisTrain$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.45 1.2 1.73 0.000124
pander::pander(t(rrAnalysisTrain$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.06 1.06 1.02 1.1
pander::pander(t(rrAnalysisTrain$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
1 1 0.996 1.01
pander::pander(rrAnalysisTrain$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.651 0.651 0.588 0.712
pander::pander(t(rrAnalysisTrain$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.691 0.598 0.784
pander::pander((rrAnalysisTrain$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.314 0.233 0.405
pander::pander((rrAnalysisTrain$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.83 0.692 0.924
pander::pander(t(rrAnalysisTrain$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.795
pander::pander(rrAnalysisTrain$surdif,caption="Logrank test")
Logrank test Chisq = 7.945448 on 1 degrees of freedom, p = 0.004821
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 122 83 95.6 1.65 7.95
class=1 46 38 25.4 6.22 7.95

Cross-Validation

rcv <- randomCV(theData=lung,
                theOutcome = Surv(time,status)~1,
                fittingFunction=BSWiMS.model, 
                trainFraction = 0.95,
                repetitions=200,
                classSamplingType = "Pro"
         )

.[++].[+++].[+++].[+].[+++].[+].[++].[+++].[+++].[+++]10 Tested: 84 Avg. Selected: 3.4 Min Tests: 1 Max Tests: 3 Mean Tests: 1.190476 . MAD: 0.4813504

.[+++].[+++].[+++].[++].[++-].[+++].[+++].[+++].[+++].[++]20 Tested: 117 Avg. Selected: 3.55 Min Tests: 1 Max Tests: 5 Mean Tests: 1.709402 . MAD: 0.4775247

.[+++].[+++].[+++].[+].[++].[+++].[++++].[++].[++].[++]30 Tested: 145 Avg. Selected: 3.533333 Min Tests: 1 Max Tests: 7 Mean Tests: 2.068966 . MAD: 0.474251

.[+++].[+++].[++].[+++].[+++].[++].[+++].[+].[++].[+++]40 Tested: 158 Avg. Selected: 3.525 Min Tests: 1 Max Tests: 8 Mean Tests: 2.531646 . MAD: 0.4758661

.[+].[+++].[+++].[++].[+++].[+++].[+++].[+++].[+++].[+++]50 Tested: 162 Avg. Selected: 3.56 Min Tests: 1 Max Tests: 8 Mean Tests: 3.08642 . MAD: 0.4761832

.[+++].[++].[++++].[+++].[+++].[+++].[+++].[+++].[+++].[+++]60 Tested: 164 Avg. Selected: 3.633333 Min Tests: 1 Max Tests: 9 Mean Tests: 3.658537 . MAD: 0.4765342

.[+++].[+++].[+++].[+++].[+++].[+++].[++].[+++].[++].[++]70 Tested: 166 Avg. Selected: 3.642857 Min Tests: 1 Max Tests: 11 Mean Tests: 4.216867 . MAD: 0.4760812

.[++].[++].[+++].[+++].[+++].[+++-].[++].[++++].[++].[++]80 Tested: 167 Avg. Selected: 3.6375 Min Tests: 1 Max Tests: 12 Mean Tests: 4.790419 . MAD: 0.4751644

.[++].[++].[+++].[++].[+++].[+++].[+++].[++].[+++].[+++]90 Tested: 167 Avg. Selected: 3.633333 Min Tests: 1 Max Tests: 12 Mean Tests: 5.389222 . MAD: 0.4751534

.[++-].[++-].[++-].[++].[+++].[+++].[+++].[++].[++].[+++]100 Tested: 167 Avg. Selected: 3.61 Min Tests: 1 Max Tests: 14 Mean Tests: 5.988024 . MAD: 0.475543

.[+].[+++].[++].[++].[+++].[++].[++-].[++].[++].[++-]110 Tested: 167 Avg. Selected: 3.563636 Min Tests: 1 Max Tests: 16 Mean Tests: 6.586826 . MAD: 0.4753595

.[+++].[+++].[+++].[+++].[+++].[++++].[+++].[++].[+++].[+++]120 Tested: 167 Avg. Selected: 3.6 Min Tests: 1 Max Tests: 16 Mean Tests: 7.185629 . MAD: 0.475331

.[+].[++].[++].[++].[++].[+++].[+++].[+++].[+++].[+++]130 Tested: 167 Avg. Selected: 3.584615 Min Tests: 1 Max Tests: 18 Mean Tests: 7.784431 . MAD: 0.4753503

.[++++].[+++].[+].[+].[+].[+].[+++].[+++].[+++].[+++]140 Tested: 167 Avg. Selected: 3.557143 Min Tests: 1 Max Tests: 18 Mean Tests: 8.383234 . MAD: 0.4754759

.[+++].[+++].[+++].[+++].[+++].[+++].[++++].[++].[++].[++]150 Tested: 168 Avg. Selected: 3.573333 Min Tests: 1 Max Tests: 19 Mean Tests: 8.928571 . MAD: 0.475539

.[+++].[++].[+++].[++].[++].[++].[+++].[++-].[++].[++++]160 Tested: 168 Avg. Selected: 3.56875 Min Tests: 1 Max Tests: 19 Mean Tests: 9.52381 . MAD: 0.4755886

.[++++].[+++].[++].[+].[+++].[+++].[+++].[++++].[++].[+++]170 Tested: 168 Avg. Selected: 3.576471 Min Tests: 1 Max Tests: 20 Mean Tests: 10.11905 . MAD: 0.4758507

.[+++].[+++].[+++].[++-].[++].[+++].[+++].[+++].[+].[+++]180 Tested: 168 Avg. Selected: 3.577778 Min Tests: 1 Max Tests: 20 Mean Tests: 10.71429 . MAD: 0.4756863

.[+].[++-].[+++].[++].[+++].[+++].[+++].[+++].[++].[++]190 Tested: 168 Avg. Selected: 3.568421 Min Tests: 1 Max Tests: 22 Mean Tests: 11.30952 . MAD: 0.4751899

.[+++].[+].[++].[+].[+].[++].[+++].[++].[+++].[+++]200 Tested: 168 Avg. Selected: 3.545 Min Tests: 1 Max Tests: 23 Mean Tests: 11.90476 . MAD: 0.4749347

stp <- rcv$survTestPredictions
stp <- stp[!is.na(stp[,4]),]

bbx <- boxplot(unlist(stp[,1])~rownames(stp),plot=FALSE)
times <- bbx$stats[3,]
status <- boxplot(unlist(stp[,2])~rownames(stp),plot=FALSE)$stats[3,]
prob <- ppoisGzero(boxplot(unlist(stp[,4])~rownames(stp),plot=FALSE)$stats[3,],h0)

rdatacv <- cbind(status,prob)
rownames(rdatacv) <- bbx$names
names(times) <- bbx$names

rrAnalysisTest <- RRPlot(rdatacv,atRate=c(0.90),
                     timetoEvent=times,
                     title="Test: Lung Cancer",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Cross-Validation Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8047 0.608 0.478 0.438 0.501
RR 1.1859 2.958 3.079 1.000 2.612
RR_LCI 0.9720 1.387 1.312 0.000 1.244
RR_UCI 1.4469 6.309 7.226 0.000 5.483
SEN 0.1983 0.959 0.967 1.000 0.959
SPE 0.8936 0.298 0.255 0.000 0.255
BACC 0.5460 0.628 0.611 0.500 0.607
NetBenefit 0.0203 0.386 0.506 0.502 0.482
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.44 1.2 1.73 0.000126
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.05 1.05 1.01 1.09
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.944 0.944 0.934 0.956
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.603 0.604 0.544 0.671
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.601 0.499 0.703
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.19 0.124 0.271
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.805
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.346444 on 1 degrees of freedom, p = 0.125569
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 140 98 103.8 0.328 2.35
class=1 28 23 17.2 1.981 2.35

Calibrating the test results

rdatacv <- cbind(status,prob,times)
calprob <- CalibrationProbPoissonRisk(rdatacv)

pander::pander(c(h0=calprob$h0,
                 Gain=calprob$hazardGain,
                 DeltaTime=calprob$timeInterval),
               caption="Cox Calibration Parameters")
h0 Gain DeltaTime
0.85 1 756
timeinterval <- calprob$timeInterval;

rdata <- cbind(status,calprob$prob)


rrAnalysisTest <- RRPlot(rdata,atRate=c(0.90),
                     timetoEvent=times,
                     title="Calibrated Test: Lung",
                     ysurvlim=c(0.00,1.0),
                     riskTimeInterval=timeinterval)

Calibrated Test Performance

pander::pander(t(rrAnalysisTest$keyPoints),caption="Threshold values")
Threshold values
  @:0.9 @MAX_BACC @MAX_RR @SPE100 p(0.5)
Thr 0.8047 0.608 0.478 0.438 0.501
RR 1.1859 2.958 3.079 1.000 2.612
RR_LCI 0.9720 1.387 1.312 0.000 1.244
RR_UCI 1.4469 6.309 7.226 0.000 5.483
SEN 0.1983 0.959 0.967 1.000 0.959
SPE 0.8936 0.298 0.255 0.000 0.255
BACC 0.5460 0.628 0.611 0.500 0.607
NetBenefit 0.0203 0.386 0.506 0.502 0.482
pander::pander(t(rrAnalysisTest$OERatio$estimate),caption="O/E Ratio")
O/E Ratio
O/E Low Upper p.value
1.45 1.21 1.74 9.55e-05
pander::pander(t(rrAnalysisTest$OE95ci),caption="O/E Mean")
O/E Mean
mean 50% 2.5% 97.5%
1.06 1.06 1.02 1.1
pander::pander(t(rrAnalysisTest$OAcum95ci),caption="O/Acum Mean")
O/Acum Mean
mean 50% 2.5% 97.5%
0.944 0.944 0.933 0.955
pander::pander(rrAnalysisTest$c.index$cstatCI,caption="C. Index")
mean.C Index median lower upper
0.603 0.603 0.539 0.665
pander::pander(t(rrAnalysisTest$ROCAnalysis$aucs),caption="ROC AUC")
ROC AUC
est lower upper
0.601 0.499 0.703
pander::pander((rrAnalysisTest$ROCAnalysis$sensitivity),caption="Sensitivity")
Sensitivity
est lower upper
0.19 0.124 0.271
pander::pander((rrAnalysisTest$ROCAnalysis$specificity),caption="Specificity")
Specificity
est lower upper
0.894 0.769 0.965
pander::pander(t(rrAnalysisTest$thr_atP),caption="Probability Thresholds")
Probability Thresholds
90%
0.805
pander::pander(rrAnalysisTest$surdif,caption="Logrank test")
Logrank test Chisq = 2.346444 on 1 degrees of freedom, p = 0.125569
  N Observed Expected (O-E)^2/E (O-E)^2/V
class=0 140 98 103.8 0.328 2.35
class=1 28 23 17.2 1.981 2.35